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Verifiable knowledge for AI agents — every claim carries its own re-derivable proof. SAVA · Confluence · NT.

🌊 acsa-ai

A worldwide flow of verifiable knowledge for AI agents

Every claim carries its own re-derivable proof — so agents, starting with code, build on facts they verify from first principles instead of sources they have to trust.

acsa.ai — live verify — zero-install python — 3.10+ stdlib only keyless — no API, no account nullius in verba

acsa.ai  ·  nullius in verba — take nobody's word for it


The idea

A fact on the internet is something you trust. A fact in ACSA is something you re-derive. Every claim is a self-verifying Drop — a signature, its evidence, and a verdict any machine reproduces offline with nothing but the Python standard library. No API, no account, no install, no authority to capture.

verified ≠ true. A verdict proves a claim's provenance and that it faithfully quotes or executes its evidence — not that it is correct about the world. Opinions and failing checks come back not_established, on purpose. That refusal is the point.

Three protocols, three properties

ACSA is a stack of three keyless protocols, each giving the network one property a self-sustaining knowledge substrate needs:

Protocol Property What it does
SAVA self-verifying one claim proves itself — Ed25519 over a canonical hash, grounded by a re-runnable check or a byte-exact quote
Confluence self-preserving trust computed from Sybil-hard independent evidence, so spam and attack can't move the signal
NT · Nullius Testimony self-running an open, keyless substrate — anyone lists a pond, nobody signs admission, no operator to capture
flowchart LR
  A["📝 Claim"] -->|"SAVA · sign + ground"| B["🔏 Drop<br/>self-verifying"]
  B -->|"NT · pinned per-pond keys"| C["🌊 Keyless view<br/>no lake key"]
  C -->|"Confluence · independent evidence"| D["📊 Trust-ranked facts<br/>spam sinks to 0"]
  D -->|"agent builds only on result 0"| A
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Prove it yourself — ~10 seconds, nothing installed

Don't take our word for it. Fetch the pinned verifier and a real check-grounded fact, and watch it re-derive on your machine:

curl -s https://acsa.ai/lake/sava_verify.py -o sava_verify.py
curl -s https://acsa.ai/lake/ponds/pyfacts.ponds.acsa.ai/drops/gauss-sum-1-100.json -o d.json
python3 sava_verify.py drop d.json \
  --trust "$(curl -s https://acsa.ai/lake/ponds/pyfacts.ponds.acsa.ai/pubkey.hex)" \
  --execute-checks --json
# → {"result": 0, "source_fidelity": "checked-by-execution", "verdict": "verified", "reasons": []}

Change one byte of d.json and the seal breaks. That's the whole idea.

How a claim grounds itself

A Drop earns verified only if its evidence re-derives. There are two honest ways — and a claim with neither is declined, not stamped:

flowchart TD
  C["A claim"] --> Q{"grounded how?"}
  Q -->|"quote"| QV["byte-exact in a cited source<br/>→ checked-ok"]
  Q -->|"check"| CV["a re-runnable snippet returns True<br/>→ checked-by-execution"]
  Q -->|"neither"| NV["not_established<br/>opinions sink here, on purpose"]
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Checks run in a locked-down sandbox and must be machine-stable — the same result on every machine, or they never verify.

How an agent consumes it — keyless, cold, from the open internet

No lake key. No operator index. The agent carries pinned per-pond keys and re-derives everything itself:

sequenceDiagram
  participant Agent
  participant acsa.ai
  Agent->>acsa.ai: GET /lake/nt-bootstrap.json
  Note over Agent: pinned {pond → pubkey}
  Agent->>acsa.ai: GET each pond_head.json
  Note over Agent: verify signature vs the PINNED key
  Agent->>acsa.ai: GET manifest.json + drops
  Note over Agent: reproduce the signed merkle_root —<br/>a host can't add, drop, or swap a claim
  Note over Agent: Confluence-rank · build only on result 0
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One line, if the engine is installed:

python -m acsa.nt https://acsa.ai/lake/nt-bootstrap.json --execute-checks

The repos

Repo What
docs start here to adopt — the pitch, a 5-minute quickstart, an integration guide, and a ready-to-fork demo pond
acsa the engine — SAVA · Confluence · NT, the reference tools, and the tests that keep them honest
acsa-pond-template fork-a-pond: stand up your own signed, cold-verifiable pond and join the lake
acsa-lake the lake: pulls admitted ponds, grounded-verifies them, publishes a cold-verifiable surface
acsa-pond-teardown a live pond: verified Ronin-bridge incident intelligence, every claim re-derivable

→ acsa.ai  ·  point your agent at it and watch it refuse anything that doesn't check out.

Popular repositories Loading

  1. acsa acsa Public

    The ACSA engine — SAVA (self-verifying), Confluence (self-preserving), NT (self-running). Reference tools, sandbox, and tests.

    Python

  2. acsa-pond-teardown acsa-pond-teardown Public

    A self-hosted SAVA pond: verified Ronin-bridge incident intelligence, every claim cold-verifiable. Forkable.

    Python

  3. acsa-lake acsa-lake Public

    A SAVA lake: pulls admitted ponds, grounded-verifies, and publishes a signed, cold-verifiable lake surface.

    Python

  4. acsa-pond-template acsa-pond-template Public template

    Fork-a-Pond: stand up your own signed, cold-verifiable SAVA pond and join the A.C.S.A. lake — no install, just python3.

    Python

  5. .github .github Public

    Org profile — acsa-ai landing page

  6. docs docs Public

    Adopt ACSA — pull verified, re-derivable facts into your AI agents. Pitch, 5-minute quickstart, integration guide, and a ready-to-fork demo pond.

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